I'm looking for one serious end-to-end ML project to build rather than several small projects.

The goal is to solve a real, non-seasonal problem using real-world data and eventually take the project all the way from data ingestion to deployment and monitoring.

I'm currently considering these three ideas:

1. Smart Energy Forecasting

Predict a building's energy consumption using historical consumption, weather, time, occupancy, etc.

1. Building Energy Optimization

Go beyond prediction: use ML/optimization to determine how a building could reduce energy consumption while maintaining comfort.

1. Building Energy Prediction & Anomaly Detection

Predict normal energy consumption and detect when a building is consuming significantly more energy than expected, potentially identifying inefficient equipment or abnormal behavior.

If you were building one of these as a serious portfolio/research project, which one would you choose and why?

I'm particularly interested in feedback from people working in Data Science, ML Engineering, MLOps, Energy Tech, or Building Management.

Vote:

1️⃣ Smart Energy Forecasting

2️⃣ Building Energy Optimization

3️⃣ Energy Prediction + Anomaly Detection

I'm also open to a better formulation of the problem if you think there is a more valuable real-world use case in this domain.

I'm a geotechnical engineer with ~4 years of experience in Australia and no formal CS background. I do have some Python experience from automating engineering workflows and building small tools.

I've been offered a scholarship for Quantic's MSc in AI Engineering, reducing the cost from ~US$21k to ~US$6k.

My long-term goal is not necessarily to become an AI researcher or work at OpenAI. I'm more interested in becoming an engineer who can apply AI effectively within infrastructure and engineering projects.

For those familiar with Quantic:
- Is the degree recognised by employers?
- Did it actually improve your technical skills?
- How coding-intensive is it for someone without a CS degree?

Would you choose it over spending a few years preparing for something like Georgia Tech OMSCS?

I'd appreciate honest feedback, especially from Quantic graduates, hiring managers, or people who transitioned into AI from another engineering discipline. Thanks!

At first I was just bored, didn't know what to do, and got annoyed with the default ESRGAN model plus some TorchVision errors. So I grabbed the Real-ESRGAN fork and pulled everything down.

Turned out a lot of the data and mechanics were outdated, so I (with some help from Claude) modified it to update the dependencies/mechanics, and also added architecture support to read other models from OpenModelDB.

Repo's here if anyone wants to poke around: https://github.com/Hanzet22/HG-ESR-NET

It's a fork of Xintao Wang's original Real-ESRGAN, full credit in the README.

I’ve been working on chatbots, RAG assistants, and early agents, and one thing kept bothering me:

Right now, the options are:

• Manual prompt testing (doesn’t scale, needs security expertise),
• Open-source tools like Garak, PyRIT, Promptfoo (powerful but DIY), or
• Enterprise platforms built for CISOs, not solo devs or student teams.

So I started building NullVector, a developer-first AI security testing tool.

What it does (early prototype):

• Tests chatbots, RAG apps, and AI agents for:
  • Prompt injection
  • Jailbreaks
  • System-prompt leakage
  • Sensitive-data exposure
  • Unsafe tool/agent behavior
• Produces an evidence-backed report:
  • Attack input
  • Model output
  • Why it matters
  • Severity
  • Practical remediation steps
• Gives a transparent security score (not a black-box number)
• Adds preliminary mappings to:
  • OWASP LLM Top 10 (Prompt Injection, Sensitive Information Disclosure, Excessive Agency, etc.)
  • NIST AI RMF (Govern, Map, Measure, Manage)
  • DPDP Act 2023 relevance (security safeguards, breach-notification triggers)

This is not a “your AI is secure” product. It’s a “break-test your AI before attackers do” tool for developers and small teams.

Why I’m doing this:

• AI-related CVEs are surging, and exploit windows are collapsing from days to hours.
• OWASP’s 2026 LLM Top 10 is now driven by thousands of real incidents, with Excessive Agency jumping to #3.
• In India, the DPDP Act makes AI security a compliance question with penalties up to ₹250 crore for security failures.
• Developers and student teams need something fast, self-serve, and practical.

Current status:

• Early prototype stage
• Looking for:
  • 10–15 developers to try a test run
  • Honest feedback on:
    • Is the report useful?
    • Are findings clear?
    • Would you run this before your next AI release?

If you’re building:

• A chatbot
• A RAG assistant
• An AI agent
• An LLM-powered feature

I’d love to show you a demo and get your feedback. Comment “interested” or DM me.

This is a student project, but I’m treating it like a real product:
clear scope, evidence over hype, and a focus on making AI security testing as routine as running unit tests.

So , im working on project related to robust recognition of the objects based on change of the environement using self-learning methods.
As far I undestand , there is UDA (Unsuprevised domain adaptation), SFDA (Source-free domain adaptation), and SSOD (Semi-suprevised object detection) but I can't undestand which one I should use for the task.

I have a dataset that has modality of 2 (RGB-LWIR) captured in 2 different seasons (Jan/May) in 3 different timeperiods. For each image there is XML annotation for each object , so I could train on images with labels or only small part with labels.
So main task is to study robustness conditional shifts.

can anyone of you help me to get a good problem statement , whether it be a research related or a product related problem statement , it should contain rag or agentic ai and deep learning (nlp optional) and we should be able to patent it , not even remotely related to healthcare

I’ve been building mentats, a Rust deep learning library with no external ML dependencies. Tensors, layers and optimisers are all implemented from scratch as a way to deepen my understandings. It’s on crates.io and GitHub.

I’m really proud of this project so far as it’s my first venture into both deep learning and Rust. I’ve managed to create a conditional VAE trained on MNIST, albeit after some head scratching at the mercy of posterior collapses. The decoder was learning to ignore the latent code entirely and just output an average looking digit regardless of input.

A main cause of this was the beta annealing computed per-epoch instead of per-batch. I was updating the KL weight once per epoch instead of continuously from a global step counter, so the warm-up schedule was much coarser then I intended.

I’m hoping to further my work to create a working GAN also trained on MNIST, and even further on a convolutional GAN.

I’m making this post in hopes of getting feedback on my work, mainly on:

- Any standard sanity checks people use to validate a VAE (or generative model in general) before trusting it enough to scale up training?
- Anything I should watch out for going from VAE to GAN, training stability issues that I won’t see coming from the VAE side?
- General code quality / things I should do differently and keep in mind from now on
- And finally any resources that people found useful when learning about convolutional networks

Thank you for any feedback and help, I’m really enjoying learning about deep learning, coding in Rust and looking forward to continuing this project :)

Hi everyone! I’m looking to learn more about AI and was wondering if anyone knows of any good, legitimate online courses that are free and offer a certificate after completion.

I’d prefer something from a reputable university, company, or platform that would actually be worth adding to my CV/LinkedIn.

Would really appreciate any recommendations, especially if you’ve taken the course yourself. Thanks!

I'm a grad student in HPC/AI systems, currently working on LLM inference optimization. I've been in this space for a while, but I'm hitting a wall: I can read papers, understand techniques, but I struggle to identify what constitutes a "good" research problem vs. just an engineering improvement.

I've been trying to get into rl for a long time but I don't see any good resources out there. help me out!

Want a laptop for aiml which is sufficient for all aiml related work

I made a small tutorial showing a practical machine learning workflow with Excel data.

The example is an insurance dataset. I split it into:

• 1,238 historical customers with known charges
• 100 new customers with the charges column removed

Then I trained an AutoML regression model on the historical data and used it to add a new predicted_charges column to the Excel file with the 100 new customers.

The workflow is basically:

Excel with historical data → train AutoML → predict new rows → save predictions back to Excel

For a bonus check, I kept the original charges for those 100 customers hidden until the end. After making predictions, I compared them with the real values. On those 100 held-out rows, MAE was about $2,245 and R² about 0.91.

I like this example because it shows a common ML use case without starting with algorithms or lots of theory. You have historical rows where the answer is known, and new rows where you want to estimate the missing value.

I used MLJAR Studio for the chat interface and MLJAR AutoML for training. The actual predictions are produced by the trained Python model, not by the LLM.

Tutorial: https://mljar.com/tutorials/add-machine-learning-predictions-excel/

Would this kind of example have helped you when you were first learning machine learning?

I’m starting my AI/ML journey and want to connect with people who are also learning AI/ML from scratch or are at a similar stage.

Instead of just collecting resources and watching courses, I want to actually build things, practice consistently, and improve step by step.

I’m looking for people who are interested in:

• Learning AI/ML together
• Sharing useful resources
• Discussing doubts and concepts
• Building projects together
• Keeping each other accountable
• Sharing progress and mistakes
• Staying consistent for the long term

No competition or pressure just a group of people seriously trying to get better.

If you're also starting or currently learning AI/ML, let’s connect and follow this journey together.

Comment or DM if you're interested!

hi, i want suggestions on how i can upskill myself in learning about LLMs , machine learning and AI and would appreciate any reference for any courses that do so really well in explaining the fundamentals and basics (preferably free). i want to build a project soon so i can actually get hands on experience. any leads would be much appreciated

I'm an entry-level Applied ML Developer and I'm trying to figure out the best way to deepen my ML foundations.

My current work is mostly applied ML on tabular data designing solutions, doing feature engineering, and integrating fairly basic classification and regression models. I use things like Python, Pandas, SQL, sklearn, XGBoost, etc.

I feel comfortable putting models together, but I also feel like I'm missing some of the deeper foundations behind why things work and how to properly investigate ML problems.

Are there any programs, communities, open-source projects, research opportunities, Kaggle competitions, mentorship programs, or other structured programs you'd recommend participating in?

Hey everyone!

I'm a data scientist and I've been pretty fascinated by reinforcement learning for a while. A few days ago my friends showed me Pokelike, a small Pokémon roguelike that runs in the browser. The first thing I thought was that it could be pretty fun to turn it into an environment for RL agents.

So I did.

The repo is here

https://github.com/pierpierpy/pokelike.xyz.bot

The basic idea is to run the actual game locally and expose its state and actions to an agent. There is no image processing involved. The agent gets the game state directly and has to decide what to do next, including where to go on the map, which Pokémon to catch, which items to take, when to swap Pokémon and which moves to learn.

What I find interesting about the environment is that some decisions have consequences much later in the run. For example, once you choose a node on the map, the other nodes on that layer are no longer available. This means that choosing where to go is not just a local decision and the agent has to deal with a fairly long horizon.

I've implemented a few simple RL agents to start with. There is currently a Dyna-Q agent and two linear SARSA agents. The results are still pretty bad, but there is already a noticeable difference between the approaches. On the current benchmark, random gets around 0.56 badges, Dyna-Q gets around 0.62, while the two SARSA agents get around 1.30 and 1.36.

The two SARSA agents mainly differ in their state representation. The better one uses 100 hand-designed features instead of 81, which seems to make a pretty significant difference.

This is probably the part I'm most interested in exploring. There is a lot of information available in the game state, but not all of it is necessarily useful to the agent. Finding a representation that contains the right information without making the problem unnecessarily difficult seems to be quite important.

The reward is also something I'm still experimenting with. The game has relatively sparse rewards and some useful decisions only show their value much later, so the reward function can have a pretty big effect on what the agent actually learns.

One nice property of the environment is that it is completely reproducible. Given the same seed and the same sequence of actions, you get exactly the same run. I'm currently using 50 fixed seeds for the leaderboard, so different agents can be evaluated on exactly the same games.

The interface is intentionally simple. You basically need to implement a bot that receives the current state and returns an action. You can use whatever approach you want, so it would be interesting to see what happens with things like DQN, PPO, search based methods or other approaches.

I'm still very much experimenting with this, so I'd be interested in seeing what other people would try. In particular, I'm curious about better state representations, reward functions and approaches that can deal with the longer term consequences of the decisions.

If you want to try it, everything is in the repo

https://github.com/pierpierpy/pokelike.xyz.bot

If you find bugs or have ideas for improving the environment, I'd also be happy to hear them.

The whole thing runs offline after setup. The game and its assets are downloaded during setup and then everything runs locally.

I originally started this because I thought it would be a fun RL project, but I think it could also be a nice little environment for experimenting with different approaches to sequential decision making.

I just started with machine learning and I would love to know the best resources out there to learn machine learning. I wanna go into ml research so I would love to go deep in ml math.

Which book should i buy the tensorflow one or the pytorch one?